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PMID: 36915362 Published · ppublish English Journal Article

Diagnostic performance of computational fluid dynamics (CFD)-based fractional flow reserve (FFR) derived from coronary computed tomographic angiography (CCTA) for assessing functional severity of coronary lesions.

Quantitative imaging in medicine and surgery ·Vol. 13 ·No. 3 ·2023-03-01 ·页码 1672-1685

Jiang J, Du C, Hu Y, Yuan H, Wang J, Pan Y, Bao L, Dong L, Li C, Sun Y, Leng X, Xiang J, Tang L, Wang J

Abstract

Fractional flow reserve (FFR) is the gatekeeper for lesion-specific revascularization decision-making in patients with stable coronary artery disease (CAD). The potential of noninvasive calculation of FFR from coronary computed tomographic angiography (CCTA) to identify ischemia-causing lesions has not been sufficiently assessed. The objective of this study was to evaluate the feasibility and diagnostic accuracy of a novel computational fluid dynamics (CFD)-based technology, termed as AccuFFRct, for the diagnosis of functionally significant lesions from CCTA, using wire-based FFR as a reference standard. A total of 191 consecutive patients who underwent CCTA and FFR measurement for suspected or known CAD were retrospectively enrolled at 2 medical centers. Three-dimensional anatomic model of coronary tree was extracted from CCTA data, CFD was applied subsequently with a novel strategy for the computation of FFR in a blinded fashion by professionals. Results were compared to invasive FFR, a threshold of ≤0.80 was used to indicate the hemodynamically relevant stenosis. On a per-patient basis, the overall accuracy, sensitivity, specificity of AccuFFRct for detecting ischemia were 91.78% (95% CI: 86.08% to 95.68%), 92.31% (95% CI: 81.46% to 97.86%) and 91.49% (95% CI: 83.92% to 96.25%), respectively; those for per-vessel basis were 91.05% (95% CI: 86.06% to 94.70%), 92.73% (95% CI: 82.41% to 97.98%) and 90.37% (95% CI: 84.10% to 94.77%), respectively. The AccuFFRct and FFR was well correlated on per-patient (r=0.709, P<0.001) and per-vessel basis (r=0.655, P<0.001). The AUC of AccuFFRct determination was 0.935 (95% CI: 0.881 to 0.969) and 0.927 (95% CI: 0.880 to 0.960) on per-patient and per-vessel basis. This novel CFD-based CCTA-derived FFR shows good diagnostic performance for detecting hemodynamic significance of coronary stenoses and may potentially become a new gatekeeper for invasive coronary angiography (ICA).

Keywords
Coronary computed tomographic angiography (CCTA) computational fluid dynamics (CFD) computed tomographic FFR fractional flow reserve (FFR)
作者与单位
共 14 位作者,点击展开单位 / ORCID
Jiang Jun
Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Du Changqing
Department of Cardiology, Zhejiang Hospital, Hangzhou, China.
Hu Yumeng
ArteryFlow Technology Co., Ltd., Hangzhou, China.
Yuan Hong
Department of Cardiology, The First People's Hospital of Linping District, Hangzhou, China.
Wang Jianhua
Department of Radiology, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, China.
Pan Yibin
Department of Cardiovascular Medicine, Jinhua Municipal Central Hospital, Jinhua, China.
Bao Lifang
Department of Electrophysiology, Jinhua Municipal Central Hospital, Jinhua, China.
Dong Liang
Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Li Changling
Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Sun Yong
Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Leng Xiaochang
ArteryFlow Technology Co., Ltd., Hangzhou, China.
Xiang Jianping
ArteryFlow Technology Co., Ltd., Hangzhou, China.
Tang Lijiang
Department of Cardiology, Zhejiang Hospital, Hangzhou, China.
Wang Jian'an
Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Article Info
Journal
Quantitative imaging in medicine and surgery
Abbr.
Quant Imaging Med Surg
ISSN
2223-4292
Published
2023-03-01
电子出版
2023-00-06
页码
1672-1685
Language
English
Country/Region
China
NLM ID
101577942
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